from ...processing_utils import ProcessorMixin




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					'''SpeechT5FeatureExtractor'''
 __A			       =					'''SpeechT5Tokenizer'''







 def __init__(		self				:   Tuple							, lowercase_				:   Optional[Any]							, lowercase_				:   Union[str, Any])						->	Any:


   """simple docstring"""







   super().__init__(lowercase_							, lowercase_)







 def __call__(		self				:   Optional[Any]							, *lowercase_				:   List[str]							, **lowercase_				:   Dict)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    kwargs.pop("audio"							, lowercase_)
   _UpperCamelCase           =    kwargs.pop("text"							, lowercase_)
   _UpperCamelCase           =    kwargs.pop("text_target"							, lowercase_)
   _UpperCamelCase           =    kwargs.pop("audio_target"							, lowercase_)
   _UpperCamelCase           =    kwargs.pop("sampling_rate"							, lowercase_)

   if audio is not None and text is not None:
     raise ValueError(
         "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?")
   if audio_target is not None and text_target is not None:
     raise ValueError(
         "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?")
   if audio is None and audio_target is None and text is None and text_target is None:
     raise ValueError(
         "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.")

   if audio is not None:
     _UpperCamelCase           =    self.feature_extractor(lowercase_							, *lowercase_							, sampling_rate=lowercase_							, **lowercase_)
   elif text is not None:
     _UpperCamelCase           =    self.tokenizer(lowercase_							, **lowercase_)
   else:
     _UpperCamelCase           =    None

   if audio_target is not None:
     _UpperCamelCase           =    self.feature_extractor(audio_target=lowercase_							, *lowercase_							, sampling_rate=lowercase_							, **lowercase_)
     _UpperCamelCase           =    targets["input_values"]
   elif text_target is not None:
     _UpperCamelCase           =    self.tokenizer(lowercase_							, **lowercase_)
     _UpperCamelCase           =    targets["input_ids"]
   else:
     _UpperCamelCase           =    None

   if inputs is None:
     return targets

   if targets is not None:
     _UpperCamelCase           =    labels

     _UpperCamelCase           =    targets.get("attention_mask")
     if decoder_attention_mask is not None:
       _UpperCamelCase           =    decoder_attention_mask

   return inputs







 def 		__UpperCAmelCase				(		self				:   Any							, *lowercase_				:   int							, **lowercase_				:   str)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    kwargs.pop("input_values"							, lowercase_)
   _UpperCamelCase           =    kwargs.pop("input_ids"							, lowercase_)
   _UpperCamelCase           =    kwargs.pop("labels"							, lowercase_)

   if input_values is not None and input_ids is not None:
     raise ValueError("Cannot process both `input_values` and `input_ids` inputs.")
   if input_values is None and input_ids is None and labels is None:
     raise ValueError(
         "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.")

   if input_values is not None:
     _UpperCamelCase           =    self.feature_extractor.pad(lowercase_							, *lowercase_							, **lowercase_)
   elif input_ids is not None:
     _UpperCamelCase           =    self.tokenizer.pad(lowercase_							, **lowercase_)
   else:
     _UpperCamelCase           =    None

   if labels is not None:
     if "input_ids" in labels or (isinstance(lowercase_							, lowercase_) and "input_ids" in labels[0]):
       _UpperCamelCase           =    self.tokenizer.pad(lowercase_							, **lowercase_)
       _UpperCamelCase           =    targets["input_ids"]
     else:
       _UpperCamelCase           =    self.feature_extractor.feature_size
       _UpperCamelCase           =    self.feature_extractor.num_mel_bins
       _UpperCamelCase           =    self.feature_extractor.pad(lowercase_							, *lowercase_							, **lowercase_)
       _UpperCamelCase           =    feature_size_hack
       _UpperCamelCase           =    targets["input_values"]
   else:
     _UpperCamelCase           =    None

   if inputs is None:
     return targets

   if targets is not None:
     _UpperCamelCase           =    labels

     _UpperCamelCase           =    targets.get("attention_mask")
     if decoder_attention_mask is not None:
       _UpperCamelCase           =    decoder_attention_mask

   return inputs







 def 		__UpperCAmelCase				(		self				:   str							, *lowercase_				:   Any							, **lowercase_				:   Dict)						->	Optional[Any]:


   """simple docstring"""







   return self.tokenizer.batch_decode(*lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   List[str]							, *lowercase_				:   Tuple							, **lowercase_				:   Union[str, Any])						->	Optional[Any]:


   """simple docstring"""







   return self.tokenizer.decode(*lowercase_							, **lowercase_)





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ = None )      ->str:




  '''simple docstring'''

  if version.parse(hfh.__version__ ).release < version.parse("0.11.0" ).release:
    # old versions of hfh don't url-encode the file path
    _UpperCamelCase           =    quote(a__ )
  return hfh.hf_hub_url(a__    ,     a__    ,     repo_type="dataset"    ,     revision=a__ )




